
Replication material for:

Esterling, Kevin M., Archon Fung and Taeku Lee, n.d., When Deliberation Produces Persuasion Rather 
Than Polarization: Measuring and Modeling Small Group Dynamics in a Field Experiment.




Please read this README_FIRST.txt file carefully. This file gives an overview of the replication materials as well
as the tools we have created for others to use the causal persuasion model in their own research.  We have organized 
the replication materials into subdirectories and each subdirectory has a separate README.txt file that provides the
details necessary for understanding how to implement the analyses within each subdirectory.  

The subdirectories contain all of the material needed to reproduce all of the results in the paper. In addition, the
replication files also contain a tutorial (including two tutorial videos) that will show you how to implement the model
for a replication and to use the model in your own research.  The subdirectoris are (in alphabetic order):

1. FarrarAnalysis -- the regression-based approach to small group persuasion which we contrast with our approach 
2. MainModel -- the main results in the paper
3. Randomeffects -- the descriptive correlates of the random effects that we report in the paper and appendix
4. RawDataAndCodebook -- the vendor-provided raw data and codebooks (names and phone numbers redacted)
5. SampleProperties -- the randomization balance tests and comparison of ideal point distributions we describe in the appendix
6. Sensitivity -- the senstivity to missing data analysis
7. Tutorial -- the tutorial

Generally, to understand the replication one would first do the tutorial (7), then examine the raw data (4),
then replicate the main model (2), after which one could explore the other subdirectories (1, 3, 5, and 6). 

Throughout the replication we mostly rely on the persuasion_v12.dta file.  This file has been copied to the 
directories where it is needed, but it is identical across the directories.

Several housekeeping notes.  First, the directory paths in each file must be edited to work with your machine.  
Second, we have only implemented these results on Windows machines so we cannot speak to how the models will
work on other operating systems. And a final housekeeping note is that we use Stata for all data management and
descriptive/regression analysis, while we use R to set up files to read into OpenBUGS and to do post-estimation
figures and analyses.  You do not need to use Stata to complete any of the tutorials or replications.  R reads in
the Stata files and in each case where we report results from Stata regressions we reproduce the output files.

IMPORTANT!  If you are not already familiar with OpenBUGS specifically, and computational Bayesain models more
generally, it is essential that you complete the Tutorial videos contained in the Tutorial subdirectory.  

The intro tutorial will show you how to use OpenBUGS, although we strongly recommend you read and understand the principles
of Bayesian inference using a textbook such as McElreath Statistical Rethinking 
(https://xcelab.net/rm/statistical-rethinking/).   The tutorial uses simulated data to show how the model recovers benchmark 
results.

The tutorial first has a set of basic regression examples that are unrelated to the paper and only used to illustrate
how to use the software.  The tutorial also contains examples that demonstrate how to implement the full model, and 
we provide three cases: one for continous outcomes, one for dichotomous, and one for ordinal outcomes (with number of 
response categories  must be greater than 3). These models take as parameters the number of pre-post questions that
you are seeking to model (must be greater than 3); the number of respondents; and for the ordinal case the number of
response categories on your Likert scale.  

The introductory tutorial video can be found at, https://youtu.be/_44_RXTWpRw

The video explaining how to implement the full model can be found at, can be found at https://youtu.be/IlO990AKurI

The tutorial examples of the full model enable users to implement the model in their own research, and
only require changing the variable names and the other parameters in the setup file.  

We also note here that the notation for the paper changed between the first and final version of the paper, and 
the replication materials for our full model use the earlier notation.  To see how the notation in the OpenBUGS 
model corresponds to the notation in the paper, you must review the continuous full model example in the tutorial.  
Once you see how the model is implemented in the tutorial, it will be straightforward to understand the model
in the replication because the structures are identical only the notation is different.

Please direct any questions or comments to kevin.esterling@ucr.edu.

Kevin M. Esterling
Archon Fung
Taeku Lee










